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A divide-and-conquer learning approach to radial basis function networks
Yiu Ming Cheung
*
, Rong Bo Huang
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
5
Citations (Scopus)
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Keyphrases
Learning Approaches
100%
Divide-and-conquer
100%
Radial Basis Function Neural Network (RBFNN)
100%
Synthetic Data
25%
Excellent Performance
25%
Linear Combination
25%
Learning Speed
25%
System Parameters
25%
Generalization Ability
25%
Networked Learning
25%
Radial Basis Function
25%
Time Series Data
25%
Structural Complexity
25%
High-dimensional Modeling
25%
Modelling Problems
25%
Hidden Layer
25%
Decomposition Rule
25%
Engineering
Radial Basis Function Network
100%
Learning Approach
100%
Subnetwork
75%
One Dimensional
25%
Original Network
25%
Radial Basis Function
25%
System Parameter
25%
Linear Combination
25%
Dimensional Modeling
25%
Hidden Layer
25%
Subspace
25%
Computer Science
Radial Basis Function
100%
Learning Approach
100%
Subnetwork
60%
Synthetic Data
20%
Linear Combination
20%
Learning Network
20%
Dimensional Modeling
20%
Outstanding Performance
20%
System Parameter
20%
Time Series Data
20%
Chemical Engineering
Radial Basis Function Network
100%